Proceedings 1995 Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems

Summary form only given. The cognitive literature has debated at some length the rival merits of models in which there are associationist connections and implicit rules and models in which there are explicit, syntactically structured rules. Human cognition represents a singular challenge to both models for reasons that only emerge when we undertake an analysis of the nature of human cognitive adap...
View full abstract»

We propose a neural network model for the processing of fuzzy data. The network parameters (weights) are standard real numbers and the spreads at the output level result exclusively from uncertainty in the input data. Our network model performs 'intelligent' inference calculations on the basis of fuzzy data and minimizes uncertainty in the final output. The number of free parameters (weights) in o...
View full abstract»

Author index

The work presented in this paper shows that fuzzy logic can be useful for the scheduling problems where the production data such as processing times is fuzzy. The chosen area of application of fuzzy logic is the hybrid control architecture of a Flexible Manufacturing System (FMS). Fuzzy logic is used for prioritization and the ordering of the jobs in the Factory Controller queue. The preliminary r...
View full abstract»

This paper describes the effective use of low level sensor information to increase reliability and safety for a shop floor application of robotic palletising. The reliability of automated machinery in shop floor applications depends to a great extent on how well the system can respond to unpredictable situations. For any robotic installation one crucial factor for reliable and safe operation is Th...
View full abstract»

This paper presents a specific example of model predictive control (MPG) of an Ultra-High Temperature (UHT) milk treatment plan using a Artificial Neural Network (ANN) as the model. Single-network and composite-network models were trained on plant data with the composite-network model performing better. Simulations of a MPC scheme using the composite network model as a prediction model show that t...
View full abstract»

In this paper, we propose a new algorithm which solves the routing problem of LSI layout design as a continuous valued constrained optimization problem. All continuous valued wires change their values simultaneously according to the dynamic equations of Lagrangian method, hence this method is suitable for neurocomputing. We show that this method can solve the small switchbox routing problems with ...
View full abstract»

In the coming 21st century, “global communication among different languages” will become more promising and more realistic. However, despite the great expectations of society for such an ideal speech translating system, there remains a great number of difficult problems related to acoustic and linguistic phenomena that need to be overcome. This is because spoken language is characteriz...
View full abstract»

We describe the application of a hardware genetic algorithm processor to handle the adaptive IIR filter problem. We have analysed a variety of configurations including varying the string word length and the introduction of multiple processor configurations. Results show that multiple genetic algorithm processor configurations work better than other configurations with lesser complexity. It is poss...
View full abstract»

In industrial sawmills, bandsaws must work at a high production rate. Two major factors which limit cutting performance are cracking and instability of the saw blades. This paper describes the results from the development of a diagnostic system which monitors blade vibration and blade tension sensor data to estimate crack length using neurocomputing techniques, to help predict blade failure. It wa...
View full abstract»

This paper describes a novel trainable controller which extends the feedback-error paradigm of Kawato (1987), called an adaptive trajectory generator. The controller can be adapted so that a controlled system shows a behavior which is specified using heuristic rules. The training algorithm is simple and computationally inexpensive. Its effectiveness is demonstrated with the control of a simulated ...
View full abstract»

There are many different approaches to cognitive mapping, arising mostly from the different philosophical backgrounds of the researchers involved. Our own research into the problem of how best to build a representation for one's experience of one's spatial environment is motivated by the need to understand how the human mind works. Neural network approaches to cognitive mapping are as varied as th...
View full abstract»

A connectionist system that is capable of reasoning about problems posed usually to expert systems is used for the purpose of dynamic cognition tasks. The architecture of such a system must contain “classical” AI representations and symbolic information processing entities. Coupling this part of the system with multiple connectionist structures implies a definition for the intercommuni...
View full abstract»

One of the shortcomings of artificial neural networks (ANNs) is the difficulty in predicting the best control parameters for a certain application. The number of combinations of parameters is very large. This makes it very inefficient and expensive to search manually by trial and error. Genetic algorithms (GAs) are an excellent and effective search technique suitable for this task. This paper desc...
View full abstract»

Some experiences of a self-trained programmer developing an expert system (ES), initially in VP Expert and then ported to LEVEL5 Object, are discussed. Moving from an incremental approach of developing code in FORTRAN to the disciplined approach required for an ES which is object oriented (OO), proved frustrating but also rewarding. A need for careful analysis and design not only provided a struct...
View full abstract»

Artificial neural networks (ANN) are used for modeling of industrial processes. However, most of the published papers deal with small or medium scale systems. One of the possible reasons, the slow learning or non convergence of large scale networks can now be overcome by the use of non-developed ANN process model may be optimized, after the elimination of non-relevant input and hidden-layer &ldquo...
View full abstract»

For computer aided design (CAD)/design automation (DA) for printed circuit board (PCB), automatic placement of parts has been a difficult problem. For realizing good placement, not only the structures but also the functions of the circuit should be grasped and some hierarchical methods for the placement should be done. This paper presents a new hierarchical method of placement of parts on the PCB ...
View full abstract»

Hopfield type neural networks for solving difficult combinatorial optimization problems have used gradient descent algorithms to solve constrained optimization problems via penalty functions. However, it is well known that the convergence to local minima is inevitable in these approaches. Lagrange programming neural networks have been proposed. They differ from the gradient descent algorithms by u...
View full abstract»

Typically, two sources of information about a system are available: some artisan knowledge and a sample of input-output data. This paper proposes a method for the amalgamation of these to synthesise a fuzzy model of the system. The artisan knowledge will likely be qualitative, of low resolution and accuracy whilst the data sample noisy and incomplete (not comprehensively covering the whole input s...
View full abstract»

The mammalian retina and visual cortex contain feature detecting neurons with a remarkable similarity to neurons in artificial neural networks for principal component analysis. Hebbian-type learning is one of the mechanisms responsible for the development of such neurons. It does not model, however, control of the number of neurons that develop in response to input. We propose a mechanism that ada...
View full abstract»

To realize a high-peformance automatic signature verification system, it is necessary that the selected features are potentially difficult to imitate. One of the advantages of online signature verification is that the virtual strokes which are left in the pen-up situation can be obtained. These virtual strokes can be memorized by the computer but are invisible to humans. So there is little possibi...
View full abstract»

This paper describes a simple neural architecture that can be used to match rules in knowledge based systems. The approach allows very large numbers of rules to be searched and matched using simple neural correlation matrix memories. The architecture is specifically designed to cope with inputs that may contain errors or be incomplete. Because the neural architecture is based on binary inputs and ...
View full abstract»

Work in the field of AI over the past twenty years has shown that many problems can be represented as constraint satisfaction problems and efficiently solved by constraint satisfaction algorithms. However, constraint satisfaction in its pure form isn't always suitable far real world problems, as they often tend to be inconsistent, which means the corresponding constraint satisfaction problems don'...
View full abstract»

This paper investigates the use of an artificial neural network (ANN) to estimate the six hour rainfall over the south-east coast of Tasmania. ANNs are becoming increasingly prominent in many areas of weather forecasting due to their potential to capture the complex relationships between the many factors that contribute to certain weather conditions. The estimations produced by the ANNs were compa...
View full abstract»